AttributeError: 'ConfigDict' object has no attribute 'dataset'

[James606240]

Thanks for your error report and we appreciate it a lot.

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1. I have searched related issues but cannot get the expected help.
2. I have read the FAQ documentation but cannot get the expected help.
3. The bug has not been fixed in the latest version.

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2. Did you make any modifications on the code or config? Did you understand what you have modified?
3. What dataset did you use?

Environment

1. Please run python mmdet/utils/collect_env.py to collect necessary environment information and paste it here.
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[James606240]

I'm training the custom dataset, but sometimes the error like that: File "C:\Users\James.conda\envs\mmcv_2\lib\site-packages\mmcv\utils\config.py", line 53, in getattr raise ex AttributeError: 'ConfigDict' object has no attribute 'dataset'

and this is my cfg.py code：

from mmcv import Config
from mmdet.apis import set_random_seed
from dataset import XMLCustomDataset

cfg = Config.fromfile('mmdetection/configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py')

print(f"Default Config:\n{cfg.pretty_text}")

# Modify dataset type and path.
cfg.dataset_type = 'XMLCustomDataset'
cfg.data_root = 'input/data_root/'

cfg.data.test.type = 'XMLCustomDataset'
cfg.data.test.data_root = 'input/data_root/'
cfg.data.test.ann_file = 'dataset/ImageSets/Main/val.txt'
cfg.data.test.img_prefix = 'dataset/'

cfg.data.train.dataset.type = 'XMLCustomDataset'
cfg.data.train.dataset.data_root = 'input/data_root/'
cfg.data.train.dataset.ann_file = 'dataset/ImageSets/Main/train.txt'
cfg.data.train.dataset.img_prefix = 'dataset/'

cfg.data.val.type = 'XMLCustomDataset'
cfg.data.val.data_root = 'input/data_root/'
cfg.data.val.ann_file = 'dataset/ImageSets/Main/val.txt'
cfg.data.val.img_prefix = 'dataset/'

cfg.data.train.pipeline = [
    dict(type='Mosaic', img_scale=(640, 640), pad_val=114.0),
    dict(
        type='RandomAffine',
        scaling_ratio_range=(0.1, 2),
        border=(-320, -320)),
    dict(
        type='MixUp',
        img_scale=(640, 640),
        ratio_range=(0.8, 1.6),
        pad_val=114.0),
    dict(type='YOLOXHSVRandomAug'),
    dict(type='RandomFlip', flip_ratio=0.0),
    dict(type='Resize', img_scale=(640, 640), keep_ratio=True),
    dict(
        type='Pad',
        pad_to_square=True,
        pad_val=dict(img=(114.0, 114.0, 114.0))),
    dict(
        type='FilterAnnotations',
        min_gt_bbox_wh=(1, 1),
        keep_empty=False),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
    ]

# Batch size (samples per GPU).
cfg.data.samples_per_gpu = 4

# Modify number of classes as per the model head.
cfg.model.bbox_head.num_classes = 6
# Comment/Uncomment this to training from scratch/fine-tune according to the

# model checkpoint path.
cfg.load_from = 'checkpoints/cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco_20201005_113243-42607475.pth'

# The original learning rate (LR) is set for 8-GPU training.
# We divide it by 8 since we only use one GPU.
cfg.optimizer.lr = 0.0008 / 8
cfg.lr_config.warmup = None
cfg.log_config.interval = 5

# The output directory for training. As per the model name.
cfg.work_dir = 'outputs/cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco'

# Evaluation Metric.
cfg.evaluation.metric = 'mAP'
cfg.evaluation.save_best = 'mAP'
# Evaluation times.
cfg.evaluation.interval = 1
# Checkpoint storage interval.
cfg.checkpoint_config.interval = 15

# Set random seed for reproducible results.
cfg.seed = 0
set_random_seed(0, deterministic=False)
cfg.gpu_ids = range(1)
cfg.device = 'cuda'
cfg.runner.max_epochs = 5

# We can also use tensorboard to log the training process
cfg.log_config.hooks = [
    dict(type='TextLoggerHook'),
    dict(type='TensorboardLoggerHook')]

# We can initialize the logger for training and have a look
# at the final config used for training
print('#'*50)
print(f'Config:\n{cfg.pretty_text}')

[jbwang1997]

Hello, could you provide more information, like the full error report and arguments of XMLCustomDataset?

[James606240]

Hello, could you provide more information, like the full error report and arguments of XMLCustomDataset?

C:\Users\James.conda\envs\mmcv_2\python.exe "D:/2021.9.1 北科大在職碩/AI 研究/Pycharm/@2_All_AI(PT)/train.py" Default Config: model = dict( type='CascadeRCNN', backbone=dict( type='ResNeSt', depth=101, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False, style='pytorch', init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://resnest101'), stem_channels=128, radix=2, reduction_factor=4, avg_down_stride=True), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], out_channels=256, num_outs=5), rpn_head=dict( type='RPNHead', in_channels=256, feat_channels=256, anchor_generator=dict( type='AnchorGenerator', scales=[8], ratios=[0.5, 1.0, 2.0], strides=[4, 8, 16, 32, 64]), bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[0.0, 0.0, 0.0, 0.0], target_stds=[1.0, 1.0, 1.0, 1.0]), loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), loss_bbox=dict( type='SmoothL1Loss', beta=0.1111111111111111, loss_weight=1.0)), roi_head=dict( type='CascadeRoIHead', num_stages=3, stage_loss_weights=[1, 0.5, 0.25], bbox_roi_extractor=dict( type='SingleRoIExtractor', roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), out_channels=256, featmap_strides=[4, 8, 16, 32]), bbox_head=[ dict( type='Shared4Conv1FCBBoxHead', in_channels=256, conv_out_channels=256, fc_out_channels=1024, norm_cfg=dict(type='SyncBN', requires_grad=True), roi_feat_size=7, num_classes=80, bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[0.0, 0.0, 0.0, 0.0], target_stds=[0.1, 0.1, 0.2, 0.2]), reg_class_agnostic=True, loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)), dict( type='Shared4Conv1FCBBoxHead', in_channels=256, conv_out_channels=256, fc_out_channels=1024, norm_cfg=dict(type='SyncBN', requires_grad=True), roi_feat_size=7, num_classes=80, bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[0.0, 0.0, 0.0, 0.0], target_stds=[0.05, 0.05, 0.1, 0.1]), reg_class_agnostic=True, loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)), dict( type='Shared4Conv1FCBBoxHead', in_channels=256, conv_out_channels=256, fc_out_channels=1024, norm_cfg=dict(type='SyncBN', requires_grad=True), roi_feat_size=7, num_classes=80, bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[0.0, 0.0, 0.0, 0.0], target_stds=[0.033, 0.033, 0.067, 0.067]), reg_class_agnostic=True, loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)) ]), train_cfg=dict( rpn=dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.7, neg_iou_thr=0.3, min_pos_iou=0.3, match_low_quality=True, ignore_iof_thr=-1), sampler=dict( type='RandomSampler', num=256, pos_fraction=0.5, neg_pos_ub=-1, add_gt_as_proposals=False), allowed_border=0, pos_weight=-1, debug=False), rpn_proposal=dict( nms_pre=2000, max_per_img=2000, nms=dict(type='nms', iou_threshold=0.7), min_bbox_size=0), rcnn=[ dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.5, neg_iou_thr=0.5, min_pos_iou=0.5, match_low_quality=False, ignore_iof_thr=-1), sampler=dict( type='RandomSampler', num=512, pos_fraction=0.25, neg_pos_ub=-1, add_gt_as_proposals=True), pos_weight=-1, debug=False), dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.6, neg_iou_thr=0.6, min_pos_iou=0.6, match_low_quality=False, ignore_iof_thr=-1), sampler=dict( type='RandomSampler', num=512, pos_fraction=0.25, neg_pos_ub=-1, add_gt_as_proposals=True), pos_weight=-1, debug=False), dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.7, neg_iou_thr=0.7, min_pos_iou=0.7, match_low_quality=False, ignore_iof_thr=-1), sampler=dict( type='RandomSampler', num=512, pos_fraction=0.25, neg_pos_ub=-1, add_gt_as_proposals=True), pos_weight=-1, debug=False) ]), test_cfg=dict( rpn=dict( nms_pre=1000, max_per_img=1000, nms=dict(type='nms', iou_threshold=0.7), min_bbox_size=0), rcnn=dict( score_thr=0.05, nms=dict(type='nms', iou_threshold=0.5), max_per_img=100))) dataset_type = 'CocoDataset' data_root = 'data/coco/' img_norm_cfg = dict( mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True) train_pipeline = [ dict(type='LoadImageFromFile'), dict( type='LoadAnnotations', with_bbox=True, with_mask=False, poly2mask=False), dict( type='Resize', img_scale=[(1333, 640), (1333, 800)], multiscale_mode='range', keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict( type='Normalize', mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) ] test_pipeline = [ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(1333, 800), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ] data = dict( samples_per_gpu=2, workers_per_gpu=2, train=dict( type='CocoDataset', ann_file='data/coco/annotations/instances_train2017.json', img_prefix='data/coco/train2017/', pipeline=[ dict(type='LoadImageFromFile'), dict( type='LoadAnnotations', with_bbox=True, with_mask=False, poly2mask=False), dict( type='Resize', img_scale=[(1333, 640), (1333, 800)], multiscale_mode='range', keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict( type='Normalize', mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) ]), val=dict( type='CocoDataset', ann_file='data/coco/annotations/instances_val2017.json', img_prefix='data/coco/val2017/', pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(1333, 800), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ]), test=dict( type='CocoDataset', ann_file='data/coco/annotations/instances_val2017.json', img_prefix='data/coco/val2017/', pipeline=[ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(1333, 800), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict( type='Normalize', mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]) ])) evaluation = dict(interval=1, metric='bbox') optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) optimizer_config = dict(grad_clip=None) lr_config = dict( policy='step', warmup='linear', warmup_iters=500, warmup_ratio=0.001, step=[8, 11]) runner = dict(type='EpochBasedRunner', max_epochs=12) checkpoint_config = dict(interval=1) log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')]) custom_hooks = [dict(type='NumClassCheckHook')] dist_params = dict(backend='nccl') log_level = 'INFO' load_from = None resume_from = None workflow = [('train', 1)] opencv_num_threads = 0 mp_start_method = 'fork' auto_scale_lr = dict(enable=False, base_batch_size=16) norm_cfg = dict(type='SyncBN', requires_grad=True)

Traceback (most recent call last): File "D:\2021.9.1 北科大在職碩\AI 研究\Pycharm\@2_All_AI(PT)\train.py", line 4, in from cfg import cfg File "D:\2021.9.1 北科大在職碩\AI 研究\Pycharm\@2_All_AI(PT)\cfg.py", line 28, in cfg.data.train.dataset.type = 'XMLCustomDataset' File "C:\Users\James.conda\envs\mmcv_2\lib\site-packages\mmcv\utils\config.py", line 53, in getattr raise ex AttributeError: 'ConfigDict' object has no attribute 'dataset'

进程已结束,退出代码1

[James606240]

and it is my test：

Model can't train：

1. mmdetection/configs/retinanet/retinanet_r101_fpn_1x_coco
2. mmdetection/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco
3. mmdetection/configs/openimages/retinanet_r50_fpn_32x2_1x_openimages
4. mmdetection/configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco

Model can train：

1. mmdetection/configs/ssd/ssdlite_mobilenetv2_scratch_600e_coco
2. mmdetection/configs/yolo/yolov3_mobilenetv2_mstrain-416_300e_coco
3. mmdetection/configs/ssd/ssd512_coco

I'am test from this web: https://debuggercafe.com/custom-dataset-training-using-mmdetection/

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